python-scikit-learnFeature selection using KBest and chi2

from sklearn import datasets, feature_selection

X, y = datasets.load_digits(return_X_y=True)

Xs = feature_selection.SelectKBest(feature_selection.chi2, k=20).fit_transform(X, y)ctrl + cgithub

`from sklearn import` import module from scikit-learn	`.load_digits(` returns digits dataset for classification
`.SelectKBest(` select features according to the k highest scores	`feature_selection.chi2` use chi2 as feature scoring function
`.fit_transform(` train and reduce given dataset to the selected number of features	`k=20` we want to have `20` best features for our dataset

Usage example

from sklearn import datasets, feature_selection

X, y = datasets.load_digits(return_X_y=True)
print(X.shape)

Xs = feature_selection.SelectKBest(feature_selection.chi2, k=20).fit_transform(X, y)
print(Xs.shape)

output

(1797, 64)
(1797, 20)

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python-scikit-learnFeature selection using KBest and chi2

Usage example

output

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